Detection of a trajectory of a supersonic projectile is carried out derived from a plurality of acoustic detection signals. From these acoustic detection signals, two or more shockwave-derived trajectory estimates can be derived. Further, a wake derived trajectory bearing estimate can be derived, from which disambiguation of the shockwave-derived trajectory estimates can be effected.
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1. A method of detecting trajectory information for a supersonic projectile, comprising: collecting acoustic detection signals from an array of detection microphones; determining from at least one of the acoustic detection signals the existence of an acoustic signal characteristic of a supersonic projectile passing on a trajectory nearby; processing a shockwave portion of each acoustic detection signal, to determine first and second shockwave-based estimates of the trajectory of the supersonic projectile; processing a wake portion of each acoustic detection signal, the wake portion being after the shockwave portion, to determine a wake-based estimate by comparing the first and second shockwave-based estimates to the wake-based estimate and selecting one of the first or second shockwave-based estimates that agrees with the wake-based estimate of the trajectory of the supersonic projectile; and resolving the first and second shockwave-based estimates using the wake-based estimate to determine a disambiguated estimate of the trajectory of the supersonic projectile.
This invention relates to detecting the trajectory of supersonic projectiles using acoustic signals. The problem addressed is the ambiguity in determining the exact trajectory of a supersonic projectile due to the shockwave and wake portions of the acoustic signal, which can lead to multiple possible trajectory estimates. The method involves collecting acoustic detection signals from an array of microphones. The system first identifies an acoustic signal characteristic of a supersonic projectile passing nearby. It then processes the shockwave portion of each acoustic signal to generate two possible trajectory estimates. Next, the wake portion of the signal, which follows the shockwave, is processed to produce a wake-based trajectory estimate. The system compares the shockwave-based estimates with the wake-based estimate and selects the shockwave estimate that best matches the wake-based estimate. This selection resolves ambiguities, resulting in a disambiguated trajectory estimate for the projectile. The approach improves accuracy by leveraging both shockwave and wake signal components to refine the trajectory determination.
2. A method in accordance with claim 1 wherein the processing of the wake portion comprises processing within a frequency range selected to distinguish from an external noise source.
The invention relates to signal processing techniques for distinguishing wake signals from external noise sources. The method involves analyzing a wake portion of a signal within a specific frequency range to isolate the wake signal from interfering noise. This is particularly useful in applications where wake signals must be detected reliably in noisy environments, such as in communication systems, sensor networks, or industrial monitoring. The frequency range is chosen to minimize interference from external noise sources, ensuring accurate detection and processing of the wake signal. The method may also include preprocessing steps to condition the signal before frequency-based analysis, such as filtering or amplification. By focusing on a selected frequency range, the technique improves signal clarity and reduces false detections caused by environmental noise. The approach is adaptable to various signal types and can be integrated into existing systems requiring robust wake signal detection. The invention enhances the reliability of wake signal processing in real-world applications where noise interference is a common challenge.
3. A method in accordance with claim 2 wherein the frequency range has a lower bound of at least 1 kHz.
This invention relates to a method for processing signals within a specific frequency range to address challenges in signal analysis or communication systems. The method involves selecting a frequency range for signal processing, where the lower bound of this range is at least 1 kHz. This ensures that the method operates within a defined high-frequency spectrum, which may be critical for applications requiring precise signal filtering, noise reduction, or high-frequency data transmission. The method may be part of a broader system that includes signal acquisition, filtering, or modulation techniques, where the selected frequency range enhances performance by focusing on frequencies above 1 kHz. This approach is particularly useful in applications where lower-frequency noise or interference must be excluded, or where high-frequency components are of primary interest. The method may be implemented in hardware, software, or a combination of both, depending on the specific application requirements. By setting a minimum lower bound of 1 kHz, the method ensures consistent and reliable signal processing across different operating conditions.
4. A method in accordance with claim 1 wherein the processing of the wake portion comprises forming a series of data blocks for each acoustic detection signal, filtering said data blocks, and converting said filtered data blocks into the frequency domain.
This invention relates to signal processing techniques for acoustic detection systems, particularly for analyzing wake portions of acoustic signals to improve detection accuracy. The method processes acoustic detection signals by dividing them into a series of data blocks. Each data block undergoes filtering to remove noise or unwanted frequencies, followed by conversion into the frequency domain to facilitate spectral analysis. This preprocessing enhances the ability to identify and characterize acoustic events, such as wake signals, by isolating relevant frequency components. The technique is useful in applications where acoustic signals must be analyzed in real-time or near-real-time, such as in surveillance, environmental monitoring, or industrial equipment diagnostics. By structuring the signal into discrete blocks and applying frequency-domain transformations, the method improves signal clarity and reduces interference from background noise, enabling more reliable detection and classification of acoustic events. The approach is particularly valuable in scenarios where traditional time-domain analysis may be insufficient due to overlapping or transient signal characteristics.
5. A method in accordance with claim 4 wherein the processing of the wake portion further comprises deriving a series of bearing estimates from the frequency domain filtered data blocks.
This invention relates to signal processing techniques for analyzing wake signals, particularly in underwater acoustic applications. The method addresses the challenge of accurately estimating the bearing of a moving object, such as a marine vessel, by processing wake signals generated by the object's movement. Wake signals are complex and often contaminated with noise, making traditional bearing estimation methods unreliable. The method involves filtering the wake signal in the frequency domain to isolate relevant components while suppressing noise. The filtered signal is then divided into data blocks, each representing a segment of the wake signal. From these filtered data blocks, a series of bearing estimates are derived. These estimates are computed by analyzing the frequency-domain characteristics of the wake signal, which provide information about the direction of the moving object. The bearing estimates are refined through further processing to improve accuracy, accounting for environmental factors and signal distortions. The technique leverages frequency-domain analysis to enhance the signal-to-noise ratio and extract directional information from wake signals. This approach improves the reliability of bearing estimation in challenging underwater environments, where traditional methods may fail due to high noise levels or signal interference. The method is particularly useful for applications such as marine surveillance, underwater navigation, and object tracking.
6. A method in accordance with claim 5 wherein the processing of the wake portion further comprises fitting a curve to the bearing estimates, and to derive from an asymptote of said curve a bearing estimate of a projectile from which said wake portion was derived.
This invention relates to projectile tracking and wake analysis, specifically improving the accuracy of bearing estimates for projectiles by analyzing their wake signatures. The problem addressed is the difficulty in precisely determining the trajectory of a projectile, particularly in scenarios where direct observation is limited or obscured. The invention focuses on processing the wake portion of a projectile's signature to derive more accurate bearing estimates. The method involves analyzing the wake portion of a projectile's signature, which is the residual disturbance left in the medium (e.g., air or water) after the projectile has passed. The wake portion is processed by fitting a mathematical curve to the bearing estimates obtained from the wake data. By analyzing the asymptote of this fitted curve, a refined bearing estimate of the projectile's trajectory is derived. This approach leverages the predictable behavior of wake disturbances to improve accuracy over traditional tracking methods that rely solely on direct measurements. The curve-fitting step involves applying mathematical models to the bearing estimates, such as polynomial or exponential functions, to smooth out noise and extract the underlying trend. The asymptote of the fitted curve represents the long-term behavior of the wake, which correlates closely with the projectile's actual bearing. This derived bearing estimate is then used to enhance tracking accuracy, particularly in scenarios where direct observation is challenging or where environmental factors introduce errors. The invention improves upon prior art by providing a more robust and accurate method for determining projectile bearings, reducing reliance on direct measurements and mitigating the effects of noise and environmental disturban
7. A method in accordance with claim 1 wherein the processing of the wake portion comprises determining existence of at least one frequency component, distinguishable from noise, and for the frequency component, associating said component with a spin speed of a projectile, as a characteristic of the motion of the projectile.
This invention relates to analyzing projectile motion by processing acoustic signals, specifically the wake portion of the sound generated by a projectile in flight. The problem addressed is the difficulty in accurately determining projectile characteristics, such as spin speed, from noisy acoustic data. The method involves extracting frequency components from the wake portion of the projectile's sound that are distinguishable from background noise. Once identified, these frequency components are associated with the projectile's spin speed, providing a measurable characteristic of its motion. The technique leverages the fact that projectile spin generates distinct acoustic signatures that can be isolated from environmental noise. By analyzing these frequency components, the method enables precise determination of spin speed, which is critical for applications such as ballistics analysis, projectile tracking, and performance evaluation. The approach improves upon traditional methods by focusing on the wake portion of the sound, where spin-related frequencies are more pronounced, and by employing noise discrimination to ensure accurate measurements. This enhances the reliability of projectile motion analysis in real-world conditions.
8. A method in accordance with claim 7 wherein the processing of the wake portion comprises determining from said processing of said wake portion, a rate of change of said spin speed, as a characteristic of the motion of the projectile.
A method for analyzing projectile motion involves processing a wake portion of a projectile's trajectory to determine a rate of change of spin speed as a characteristic of the projectile's motion. The wake portion refers to the region behind the projectile where aerodynamic effects influence its behavior. By analyzing this region, the method extracts dynamic properties of the projectile, specifically how its spin speed changes over time. This rate of change provides insights into the projectile's stability, aerodynamic efficiency, and potential deviations from an intended path. The method may involve capturing high-resolution data from sensors or imaging systems to track the wake's behavior and derive the spin speed variation. This analysis helps in assessing projectile performance, improving accuracy, and identifying factors affecting flight stability. The technique is applicable in fields such as ballistics, aerodynamics, and defense technology, where understanding projectile dynamics is critical for design optimization and predictive modeling.
9. A method in accordance with claim 8 comprising determining a miss distance estimate, being an estimate of the closest distance between the projectile and the detection microphones, and processing the rate of change of spin speed with the miss distance estimate to determine an estimate of distance of projectile origin from the detection microphones.
This invention relates to acoustic detection and localization of projectiles, specifically estimating the origin distance of a projectile based on its spin characteristics and acoustic signatures. The problem addressed is accurately determining the launch point of a projectile using microphone arrays, where traditional methods may struggle with precision due to environmental factors or projectile dynamics. The method involves detecting the projectile using an array of microphones and analyzing the acoustic signals to determine the projectile's spin speed and its rate of change. The spin speed is derived from the Doppler shift in the detected acoustic signals, which varies as the projectile rotates. The rate of change of spin speed is then processed alongside a miss distance estimate—the closest distance between the projectile and the detection microphones—to calculate the projectile's origin distance from the microphones. The miss distance estimate is derived from the time difference of arrival (TDOA) of the acoustic signals at the microphones, providing a spatial reference for the projectile's trajectory. By combining the spin dynamics and acoustic localization, the method improves the accuracy of origin distance estimation, particularly for high-speed projectiles where traditional localization techniques may be less effective. The approach leverages both temporal and spectral features of the detected signals to enhance precision in determining the projectile's launch point.
10. A method in accordance with claim 8 comprising determining a miss distance estimate, being an estimate of the closest distance between the projectile and the detection microphones, and processing the miss distance estimate with the trajectory estimate to obtain a target bearing estimate.
A method for estimating the bearing of a target using projectile trajectory and acoustic detection involves determining the closest distance between a projectile and detection microphones, referred to as the miss distance estimate. This estimate is derived from the projectile's trajectory, which is calculated based on the projectile's launch parameters and environmental conditions. The trajectory estimate includes the projectile's flight path, velocity, and position over time. The miss distance estimate is then processed alongside the trajectory estimate to refine the target bearing estimate, which indicates the direction of the target relative to the detection system. This approach improves the accuracy of target localization by combining acoustic detection data with trajectory modeling, addressing challenges in determining precise target bearings in dynamic environments. The method is particularly useful in military or surveillance applications where real-time target tracking is critical. By integrating missile trajectory predictions with acoustic sensor data, the system enhances the reliability of target identification and engagement systems.
11. A method in accordance with claim 10 and comprising determining a target position estimate from the target bearing estimate.
A method for estimating the position of a target using bearing measurements. The method addresses the challenge of accurately determining a target's location when only bearing information is available, which is common in applications like radar, sonar, or navigation systems. The process involves collecting multiple bearing measurements from different observation points or over time, then processing these measurements to reduce errors and improve accuracy. The method includes filtering the bearing measurements to remove noise and outliers, and then combining the filtered measurements to generate a refined bearing estimate. This refined estimate is used to calculate a target position estimate, which provides a more precise location of the target compared to individual bearing measurements. The technique is particularly useful in scenarios where direct range measurements are unavailable or unreliable, ensuring reliable target tracking and positioning in dynamic environments. The method leverages statistical techniques and filtering algorithms to enhance the accuracy of the position estimate, making it suitable for applications requiring high precision, such as military surveillance, autonomous navigation, and environmental monitoring.
12. A method in accordance with claim 1 wherein the processing of the wake portion comprises determining existence of at least one frequency component, distinguishable from noise, characteristic of tumbling motion of a projectile, as a characteristic of the motion of the projectile.
This invention relates to projectile motion analysis, specifically detecting tumbling motion in projectiles using frequency components. The method processes a wake portion of a projectile's flight to identify frequency components that distinguish tumbling motion from noise. Tumbling motion occurs when a projectile rotates or spins unpredictably during flight, which can affect accuracy and trajectory. The method analyzes the wake portion, which is the region of disturbed air or fluid behind the projectile, to extract frequency data. By isolating frequency components unique to tumbling motion, the method differentiates between normal flight and tumbling. This helps in assessing projectile stability, improving targeting systems, and enhancing ballistic modeling. The technique involves signal processing to filter out noise and identify characteristic frequencies associated with tumbling. This allows for real-time or post-flight analysis of projectile behavior, aiding in performance evaluation and system calibration. The method is applicable in military, aerospace, and sports applications where projectile stability is critical.
13. A method in accordance with claim 7 , comprising determining from said identified one or more characteristics of the motion of the projectile, characteristics of a candidate device corresponding with the source of said projectile.
This invention relates to projectile tracking and source identification, addressing the challenge of determining the origin of a projectile based on its motion characteristics. The method involves analyzing the motion of a projectile in flight to extract specific characteristics, such as trajectory, velocity, or acceleration patterns. These characteristics are then compared against known profiles of candidate devices (e.g., launchers, firearms, or other projectile sources) to identify the most likely source. The system may use sensors, such as radar, lidar, or optical tracking, to capture motion data in real time. Advanced algorithms process this data to distinguish between different types of devices based on their unique motion signatures. The method may also incorporate environmental factors, such as wind or terrain, to refine the identification process. By correlating projectile motion with device-specific patterns, the system enables accurate source attribution, which is critical for applications in security, defense, and law enforcement. The approach improves upon traditional methods by leveraging dynamic motion analysis rather than relying solely on static features like projectile shape or composition. This enhances detection accuracy and reduces false positives in identifying the origin of projectiles.
14. A method in accordance with claim 1 and comprising monitoring, over time, the trajectory of the projectile, detecting a phase in which the trajectory remains unchanged, associating entry into that phase with strike of said projectile on a target, and determining an estimate of target position.
This invention relates to projectile tracking and target detection systems, specifically addressing the challenge of accurately determining the position of a target after a projectile strike. The method involves continuously monitoring the trajectory of a projectile over time to detect a phase where the trajectory remains unchanged. This phase is associated with the projectile striking a target, as the projectile's motion ceases or stabilizes upon impact. By identifying this phase, the system can estimate the target's position based on the projectile's final trajectory state. The method improves target detection accuracy by leveraging trajectory stability as an indicator of impact, reducing reliance on external sensors or complex algorithms. The system may also incorporate additional tracking features, such as initial trajectory prediction and real-time adjustments, to enhance precision. This approach is particularly useful in military, surveillance, or industrial applications where precise target positioning is critical. The invention provides a reliable way to confirm projectile impact and determine target location without requiring additional hardware or extensive computational resources.
15. A gunshot detector operable to detect trajectory information for a projectile on a supersonic trajectory, the detector comprising: a plurality of acoustic transducers, arranged in an array in a reference plane, each transducer being operable to convert acoustic excitations to electrical detection signals; a signal processor responsive to detecting the existence of an acoustic signal characteristic of a supersonic projectile passing on a trajectory nearby, operable to process detection signals from the acoustic transducers to determine: first and second shockwave-based estimates of the trajectory of the supersonic projectile based on processing a shockwave portion of each acoustic detection signal; a wake-based estimate of the trajectory of the supersonic projectile by processing a wake portion of each acoustic detection signal, the wake portion being after the shockwave portion; and on the basis of the first and second shockwave-based estimates on the one hand, and the wake-based estimate on the other hand, a disambiguated estimate of the trajectory of the supersonic projectile by comparing the first and second shockwave-based estimates to the wake-based estimate and selecting one of the first or second shockwave-based estimates that agrees with the wake-based estimate.
A gunshot detection system is designed to determine the trajectory of supersonic projectiles, such as bullets, by analyzing acoustic signals generated during flight. The system addresses the challenge of accurately tracking high-speed projectiles, which produce distinct shockwaves and wake patterns as they travel. The detector includes an array of acoustic transducers arranged in a reference plane, each converting acoustic excitations into electrical signals. A signal processor evaluates these signals to extract trajectory information. The processor first identifies shockwave portions of the acoustic signals and generates two shockwave-based trajectory estimates. It then processes the wake portions, which follow the shockwaves, to produce a wake-based trajectory estimate. To resolve ambiguities in the shockwave estimates, the processor compares them against the wake-based estimate and selects the shockwave estimate that aligns with the wake-based trajectory. This disambiguation step ensures accurate trajectory determination by leveraging both shockwave and wake data. The system enhances precision in tracking supersonic projectiles, improving applications such as threat detection and forensic analysis.
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July 24, 2019
April 5, 2022
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